The present invention relates generally to image systems and, more particularly, to systems and methods for automatically transforming computed tomography (CT) studies of a pelvis region to a common reference frame.
During clinical diagnosis, a patient's internal anatomy is imaged to determine how a disease has progressed. An infected tissue (such as tumor mass) shows some differences from a normal tissue. Also, the patient may have some type of individual differences or abnormalities regarding even healthy tissues.
Several modalities are used to generate images of the patient's internal anatomy or functionality, suitable for diagnostic purposes, radiotherapy treatment, or for surgical planning. Exemplary modalities include conventional X-ray plane film radiography; computed tomography (“CT”) imaging, magnetic resonance imaging (“MRI”); and nuclear medicine imaging techniques, such as positron emission tomography (“PET”) and single photon emission computed tomography (“SPECT”).
In a case of radiation treatment (“RT”) planning, CT imaging is generally used because an image pixel gray value (Hounsfield Units) is a direct function of a radiation dose calculation. A CT image is three dimensional (3D), more precisely, the CT image is a collection of adjacent transaxial two dimensional (2D) slices. Clinicians undertake a process of recombining anatomical elements of 2D slices to form a 3D object or an organ to get anatomical data about the patient being treated. The process of recombining anatomical elements as stated above is usually termed a reconstruction.
RT planning typically involves, clinicians such as, for example, radiologists, dosimetrists or radiotherapists, tracing outlines of a few critical structures on a number of image slices. Manually tracing the outlines on a contiguous set of 2D slices and then combining them can be time consuming and labor intensive. Time and labor increase significantly both as the number of image slices increase, and as a number and size of an organ, tumor, etc. in an anatomical area of interest increases. Quality of the outlining and quality of a produced 3D object depend on a resolution and contrast of the 2D slices, and on knowledge and judgment of the clinician performing the reconstruction.
Using an automated image segmentation could save time and labor that would otherwise be needed if using manual tracing. Also, automated image segmentation could increase precision (intra-operator repeatability and inter-operator reproducibility) by eliminating subjectivity of the clinician.
Automated image segmentation of organs near the pubic bone face certain challenges. Organs such as, for example, a bladder and a prostate, are located in a soft tissue environment wherein resolution against surrounding structures has poor contrast since neighboring organs have similar density values. Additionally, a partial volume effect may distort borders between organs. The partial volume effect occurs because along borders between organs, the Hounsfield Unit values are a weighted average of density values of neighboring volumes. Furthermore, shape and position of organs such as, for example, the prostate may change periodically. Characteristics of abdominal organs also change from patient to patient including for example, shape, size and location of the organ. Imaging parameters of CT machines vary as well.
Methods have been developed to use statistical data gathered from images of numerous patients in order to assist in image reconstruction. However, the use of statistical data in a manner described above requires alignment of the images. Thus, it is desirable to obtain a method to automatically transform CT studies of the pelvis region to a common reference frame.